Uniform convergence rates and uniform adaptive estimation in mixtures of regressions
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Philipps-Universität Marburg
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In this thesis, we develop theoretical tools to examine estimators in non-parametric regression models in regard of uniform convergence rates and uniform adaptivity with respect to the smoothness of the parameter functions. Subsequently, those are applied to non-parametric regression models with Hölder-smooth parameter functions. One model is a mixture of Gaussian regressions and the other model is a mixture model with two components and an unspecified symmetric error distribution.
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This item has been published with the following license: In Copyright